Semantic relation identification for consecutive predicative constituents in Chinese

In this paper, we propose a general methodology for designing semantic role/relation system. Based on this methodology, we establish a succinct semantic relation system for consecutive predicative constituents for Chinese, which includes serial verb construction, discourse construction, and other constructions describing serial events. This semantic relation system has 13 middle-level classes and 24 fine-grained sub-classes in contrast to conventional complex classification schemes and meets the uniqueness and completeness criteria of semantic relation identification. We conduct experiments on our system by training four annotators in 1 h to label 200 sentences extracted from Sinica Treebank and HIT-CDTB. With the help of our predesigned feature-based decision tree and a connective markers checklist, the annotators attain a 73% consistency with the reference standard annotation and substantial agreement by Cohen’s kappa coefficient for middle-level labeling. By analyzing the labeling error types, we slightly revise our classification scheme and propose six methods to improve the classification and labeling system, hoping to achieve even better agreement in the future.